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School of Engineering and Informatics (for staff and students)

Advanced Natural Language Processing (968G5)

Advanced Natural Language Processing

Module 968G5

Module details for 2021/22.

15 credits

FHEQ Level 7 (Masters)

Module Outline

Advanced Natural Language Processing builds on the foundations provided by the Applied Natural Language Processing module. Students will develop their knowledge and understanding of key topics including word sense disambiguation, vector space models of semantics, named entity recognition, topic modelling and machine translation. Seminars will provide in-depth discussion of research papers related to the key topics and also general issues that arise when developing natural language processing tools, including: hypothesis testing; data smoothing techniques; domain adaptation; generative versus discriminative learning; and semi-supervised learning. Labs will provide the opportunity for students to improve their python programming skills, experiment with some off-the-shelf technology and develop research skills.

Module learning outcomes

Demonstrate a systematic knowledge and understanding of key challenges in the field of natural language processing (NLP) and critical awareness of current approaches to tackling these challenges.

Critically analyse state-of-the-art NLP technologies and critically assess their application to novel problems involving large quantities of realistic data.

Critically evaluate the effectiveness of an approach through the design and application of suitable experiments.

Synthesise and critically assess state-of-the-art technologies for a given NLP problem based on primary scientific literature.

TypeTimingWeighting
Coursework100.00%
Coursework components. Weighted as shown below.
ReportT2 Week 11 60.00%
EssayA2 Week 2 40.00%
Timing

Submission deadlines may vary for different types of assignment/groups of students.

Weighting

Coursework components (if listed) total 100% of the overall coursework weighting value.

TermMethodDurationWeek pattern
Spring SemesterSeminar2 hours11111111111
Spring SemesterLaboratory2 hours11111111111

How to read the week pattern

The numbers indicate the weeks of the term and how many events take place each week.

Prof Julie Weeds

Assess convenor
/profiles/116624

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School of Engineering and Informatics (for staff and students)

School Office:
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